CN111339500A - Air pollution tracing method and device, computer equipment and storage medium - Google Patents
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Abstract
The application relates to an air pollution tracing method, an air pollution tracing device, computer equipment and a storage medium. The method comprises the following steps: processing historical air quality data based on a Gaussian process regression algorithm, and determining relevant air stations which correspond to all the air stations and meet the relevance condition; performing matrix processing on the quality data at each t moment and the air quality data at each t-1 moment to obtain a diffusion matrix of the polluted air station at the t moment; correcting the diffusion matrix according to the geographical position information of the polluted air station at the t moment, the geographical position information of the related air station of the polluted air station at the t moment, the wind speed at the t-1 moment and the wind direction at the t-1 moment; and obtaining a decision matrix of the polluted air station at the t moment according to the corrected diffusion matrix, and determining a pollution source air station of the polluted air station at the t moment according to the decision matrix, thereby realizing fast and accurate tracing to a pollution source.
Description
Technical Field
The application relates to the technical field of air treatment, in particular to an air pollution tracing method, an air pollution tracing device, computer equipment and a storage medium.
Background
With the development of industry, air pollution becomes more and more serious, and the treatment of air pollution becomes one of the environmental problems to be solved urgently, at present, a mode mainly adopted by air monitoring is to widely deploy a micro air station in an area needing to be monitored, change of air quality is monitored through the micro air station, when the air quality is monitored to be reduced, a prevention measure is timely taken to prevent the air quality from further deteriorating, in the process, how to quickly and accurately find a pollution source becomes a key for timely taking the prevention measure, but in the implementation process, an inventor finds that at least the following problems exist in the traditional technology: the traditional technology can not find the pollution source quickly and accurately.
Disclosure of Invention
In view of the above, there is a need to provide an air pollution tracing method, an air pollution tracing apparatus, a computer device and a storage medium, which can quickly and accurately find a pollution source.
An air pollution tracing method comprises the following steps:
acquiring historical air quality data of each air station in an area to be monitored, processing the historical air quality data based on a Gaussian process regression algorithm, and determining relevant air stations which meet the relevance condition and correspond to each air station;
acquiring the t-th time quality data and the t-1-th time air quality data of the polluted air station at the t-th time and the relevant air stations of the polluted air station at the t-th time, and performing matrix processing on the t-th time quality data and the t-1-th time air quality data to obtain a diffusion matrix of the polluted air station at the t-th time;
correcting the diffusion matrix according to the geographical position information of the polluted air station at the t moment, the geographical position information of the related air station of the polluted air station at the t moment, the wind speed at the t-1 moment and the wind direction at the t-1 moment;
obtaining a decision matrix of the polluted air station at the t moment according to the corrected diffusion matrix, and determining a pollution source air station of the polluted air station at the t moment according to the decision matrix;
and taking the pollution source air station of the polluted air station at the t moment as the polluted air station at the t-1 moment, and determining the pollution source air station of the polluted air station at the t-1 moment until the polluted air station at the 1 moment is obtained.
In one embodiment, the step of obtaining historical air quality data of each air station in an area to be monitored, processing the historical air quality data based on a gaussian process regression algorithm, and determining relevant air stations corresponding to each air station and meeting a relevance condition includes the steps of:
processing historical air quality data based on a Gaussian process regression algorithm to obtain a hyper-parameter of the historical air quality data;
performing regression processing on the hyper-parameters by adopting a maximum edge likelihood method based on historical air quality data to obtain correlation coefficients among the air stations;
and determining the air station with the correlation coefficient meeting the correlation condition with the air station as a relevant air station.
In one embodiment, the step of performing matrix processing on the quality data at each time t and the air quality data at each time t-1 to obtain a diffusion matrix of the contaminated air station at the time t includes the steps of:
and performing matrix processing on the quality data at each t-th moment and the air quality data at each t-1-th moment based on a random infection SIR equation to obtain a diffusion matrix of the polluted air station at the t-th moment.
In one embodiment, the step of performing correction processing on the diffusion matrix according to the geographical location information of the polluted air station at the time t, the geographical location information of the relevant air station of the polluted air station at the time t, the wind speed at the time t-1 and the wind direction at the time t-1 includes the steps of:
obtaining a first correction parameter according to the geographical position information of the polluted air station at the t moment, the geographical position information of the relevant air station of the polluted air station at the t moment and the wind direction at the t-1 moment;
obtaining a second correction parameter according to the geographical position information of the polluted air station at the t moment, the geographical position information of the related air station of the polluted air station at the t moment and the wind speed at the t-1 moment;
and correcting the diffusion matrix according to the first correction parameter and the second correction parameter.
In one embodiment, the step of obtaining the first correction parameter according to the geographical location information of the polluted air station at the time t, the geographical location information of the air station related to the polluted air station at the time t and the wind direction at the time t-1 includes the steps of:
obtaining a direction vector between the polluted air station at the t moment and the related air station thereof according to the geographical position information of the polluted air station at the t moment and the geographical position information of the related air station of the polluted air station at the t moment;
and carrying out vector processing on the direction vector and the wind direction vector of the wind direction at the t-1 th moment to obtain a first correction parameter.
In one embodiment, the step of obtaining the second correction parameter according to the geographical location information of the polluted air station at the time t, the geographical location information of the relevant air station of the polluted air station at the time t and the wind speed at the time t-1 comprises the steps of:
obtaining the distance between the polluted air station at the t moment and the related air station thereof according to the geographical position information of the polluted air station at the t moment and the geographical position information of the related air station of the polluted air station at the t moment;
and obtaining a second correction parameter according to the distance and the wind speed at the t-1 moment.
In one embodiment, the step of obtaining a decision matrix of the air station contaminated at the time t according to the corrected diffusion matrix, and determining a contamination source air station of the air station contaminated at the time t according to the decision matrix includes the steps of:
transposing the corrected diffusion matrix to obtain a transposed matrix, and taking the difference value between the corrected diffusion matrix and the transposed matrix as a decision matrix;
if the decision matrix with the value larger than zero exists, determining the relevant air station corresponding to the maximum value in the decision matrix as the pollution source air station of the polluted air station at the t moment;
and if the values of the decision matrix are all less than or equal to zero, the polluted air station at the t moment is the polluted air station at the 1 moment.
An air pollution tracing apparatus comprising:
the data acquisition module is used for acquiring historical air quality data of each air station in an area to be monitored;
the relevant air station determining module is used for processing historical air quality data based on a Gaussian process regression algorithm and determining relevant air stations which meet the relevance condition and correspond to each air station;
the diffusion matrix acquisition module is used for acquiring the t-th time quality data of the polluted air station at the t-th time and the t-1-th time air quality data of the relevant air stations of the polluted air station at the t-th time, and performing matrix processing on the t-th time quality data and the t-1-th time air quality data to obtain a diffusion matrix of the polluted air station at the t-th time;
the correction module is used for correcting the diffusion matrix according to the geographical position information of the polluted air station at the t moment, the geographical position information of the relevant air station of the polluted air station at the t moment, the wind speed at the t-1 moment and the wind direction at the t-1 moment;
the pollution source air station acquisition module is used for obtaining a decision matrix of the polluted air station at the t moment according to the corrected diffusion matrix and determining the pollution source air station of the polluted air station at the t moment according to the decision matrix;
and the circulating source tracing module is used for taking the polluted source air station of the polluted air station at the t moment as the polluted air station at the t-1 moment, determining the polluted source air station of the polluted air station at the t-1 moment until the polluted air station at the 1 moment is obtained.
A computer device comprising a memory storing a computer program and a processor implementing the steps of the method when the processor executes the computer program.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method.
One of the above technical solutions has the following advantages and beneficial effects:
the air pollution tracing method provided by the embodiments of the application comprises the following steps: acquiring historical air quality data of each air station in an area to be monitored, processing the historical air quality data based on a Gaussian process regression algorithm, and determining relevant air stations which meet the relevance condition and correspond to each air station; acquiring the t-th time quality data and the t-1-th time air quality data of the polluted air station at the t-th time and the relevant air stations of the polluted air station at the t-th time, and performing matrix processing on the t-th time quality data and the t-1-th time air quality data to obtain a diffusion matrix of the polluted air station at the t-th time; correcting the diffusion matrix according to the geographical position information of the polluted air station at the t moment, the geographical position information of the related air station of the polluted air station at the t moment, the wind speed at the t-1 moment and the wind direction at the t-1 moment; obtaining a decision matrix of the polluted air station at the t moment according to the corrected diffusion matrix, and determining a pollution source air station of the polluted air station at the t moment according to the decision matrix; and taking the pollution source air station of the polluted air station at the t moment as the polluted air station at the t-1 moment, determining the pollution source air station of the polluted air station at the t-1 moment until the polluted air station at the 1 moment is obtained, so that the pollution source can be quickly and accurately traced back, the prevention and treatment measures can be carried out on the pollution source more timely, and the further pollution deterioration is avoided.
Drawings
FIG. 1 is a schematic flow chart of an embodiment of an air pollution tracing method;
FIG. 2 is a schematic flow chart illustrating the steps for identifying the associated air station in one embodiment;
FIG. 3 is a flow chart illustrating the diffusion matrix correction step in one embodiment;
FIG. 4 is a schematic flow chart illustrating steps for identifying a source air station in one embodiment;
FIG. 5 is a block diagram of an embodiment of an air pollution tracing apparatus;
FIG. 6 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
In order to solve the problem that the conventional technology cannot find a pollution source quickly and accurately, in one embodiment, as shown in fig. 1, an air pollution source tracing method is provided, which includes the following steps:
step S110, acquiring historical air quality data of each air station in the area to be monitored, processing the historical air quality data based on a Gaussian process regression algorithm, and determining the relevant air stations which meet the relevance condition and correspond to each air station.
It should be noted that the area to be monitored may be a certain town, a certain county, a certain city, a certain direct prefecture city, or an area which is independently divided to need to be monitored. Air stations are widely deployed in the area to be monitored, for example, in a grid-like distribution. The air station monitors the air quality of the area where the air station is located, stores the monitored air quality data in an internal storage device, and is called, or uploads the monitored air quality data to a server. And the computer equipment for background monitoring acquires the air quality data monitored by all the air stations in the area to be monitored to analyze the air quality change in the area to be monitored, so that the air quality is monitored. Wherein the air quality data comprises inhalable particle content, fine particle content, nitrogen dioxide content, sulfur dioxide content, carbon monoxide content, ozone content or volatile organic compound content and the like.
The relevant air station of the certain air station means an air station that is adjacent to the certain air station in the air pollution propagation path, and the relevant air station satisfies a correlation condition indicating a probability of propagation of the air pollution from the relevant air station to the certain air station. And analyzing historical air quality data of each air station in the area to be monitored through a Gaussian process regression algorithm to obtain related air stations of each air station. An air station includes one or more associated air stations.
In one example, as shown in fig. 2, the step of obtaining historical air quality data of each air station in the area to be monitored, processing the historical air quality data based on a gaussian process regression algorithm, and determining the relevant air stations meeting the correlation condition corresponding to each air station includes the steps of:
and step S210, processing the historical air quality data based on a Gaussian process regression algorithm to obtain the hyperparameter of the historical air quality data.
It should be noted that, in the field of machine learning, a Gaussian Process (GP) regression algorithm refers to a machine learning method developed based on a Gaussian random Process and a bayes learning theory. In the theory of statistics, the process of the gaussian process regression algorithm is as follows: the distribution of any finite variable set is Gaussian distribution, namely, the joint probability distribution of the process state of any integer and any random variable family and the corresponding time follows n-dimensional Gaussian distribution. All statistical features of the gaussian process regression algorithm are determined entirely by its mean and covariance functions, which are expressed by the following definitional equations:
wherein the content of the first and second substances,
represents a time of day;
joint probabilities representing process states;
represents the mean value;
representing a covariance function;
representing a gaussian process function.
After the historical air quality data are processed by a Gaussian process regression algorithm, the historical air quality data conform to Gaussian distribution, and the expression is as follows:
wherein the content of the first and second substances,
which is indicative of the mass of the air,
indicating the predicted air quality at each air station,
representing the total number of air stations in the area to be monitored,
represents the mean value of the air mass;
a covariance function representing the air quality.
The hyper-parameter represents the correlation of two air stations at different time instants, and the expression is as follows:
wherein the content of the first and second substances,
as a function of covariance, w is the correlation coefficient of the two air stations,
and x is the air quality of two different air stations.
And step S220, performing regression processing on the hyper-parameters by adopting a maximum edge likelihood method based on historical air quality data to obtain correlation coefficients among the air stations.
It should be noted that the correlation coefficient represents the magnitude of correlation between any two air stations on the air pollution propagation path, i.e., the probability of air pollution propagating from one air station to another air station.
In step S230, the air station whose correlation coefficient with the air station satisfies the correlation condition is determined as a relevant air station.
The air station having a correlation coefficient satisfying the correlation condition with respect to a certain air station is selected as the relevant air station for the certain air station. Further, after the relevant air stations corresponding to all the air stations are confirmed, all the relevant air stations are constructed into a correlation matrix.
And step S120, acquiring the t-th time quality data of the polluted air station at the t-th time and the air quality data of the relevant air station of the polluted air station at the t-th time and the t-1-th time air quality data, and performing matrix processing on each t-th time quality data and each t-1-th time air quality data to obtain a diffusion matrix of the polluted air station at the t-th time.
It should be noted that the tth time is a time when the air station collects the air quality data for the tth time. The t-th time can be the time when the air station collects the air quality data at a certain time historically or the time when the air station collects the air quality data at the current time. The tth moment is the moment when the air station historically collects the air quality data at a certain time and is used for analyzing the historical condition of the air quality; and the t moment is the moment when the air station collects the air quality data at the current time and is used for monitoring the current air quality. Generally, air monitoring is a real-time process for monitoring the current air quality, so as to realize prevention and control on the air quality at the current moment, and preferably, the t-th moment is the current moment.
In one example, the step of performing matrix processing on the quality data at each time t and the air quality data at each time t-1 to obtain a diffusion matrix of the polluted air station at the time t includes the steps of:
and performing matrix processing on the quality data at each t-th moment and the air quality data at each t-1-th moment based on a random infection SIR equation to obtain a diffusion matrix of the polluted air station at the t-th moment.
It should be noted that the expression of the SIR equation is:
wherein the content of the first and second substances,
representing air mass column vectors of air stations at time t, i.e.
Wherein, in the step (A),
indicating air stationnAir quality at time t.
And S130, correcting the diffusion matrix according to the geographical position information of the polluted air station at the t moment, the geographical position information of the relevant air station of the polluted air station at the t moment, the wind speed at the t-1 moment and the wind direction at the t-1 moment.
It should be noted that, since the air pollution propagation is affected by the wind speed and the wind direction, under the condition of different wind speeds and wind directions, the correlation coefficient between the same two air stations changes, and in order to improve the accuracy of the pollution source tracing, the diffusion matrix needs to be corrected.
The wind speed at the t-1 time is the wind speed of the relevant air station at the time of collecting the air quality data t-1 times, and the wind direction at the t-1 time is the wind direction of the relevant air station at the time of collecting the air quality data t-1 times.
In one example, as shown in fig. 3, the step of performing correction processing on the diffusion matrix according to the geographical location information of the polluted air station at the time t, the geographical location information of the relevant air station of the polluted air station at the time t, the wind speed at the time t-1 and the wind direction at the time t-1 includes the steps of:
step S310, obtaining a first correction parameter according to the geographical position information of the polluted air station at the t moment, the geographical position information of the relevant air station of the polluted air station at the t moment and the wind direction at the t-1 moment.
Specifically, the step of obtaining the first correction parameter according to the geographical location information of the polluted air station at the t-th moment, the geographical location information of the air station related to the polluted air station at the t-th moment, and the wind direction at the t-1 th moment includes the steps of:
obtaining a direction vector between the polluted air station at the t moment and the related air station thereof according to the geographical position information of the polluted air station at the t moment and the geographical position information of the related air station of the polluted air station at the t moment;
and carrying out vector processing on the direction vector and the wind direction vector of the wind direction at the t-1 th moment to obtain a first correction parameter.
Further, the first correction parameter is obtained based on the following formula:
wherein the content of the first and second substances,
represents a first correction parameter;
representing an orientation vector;
a wind direction vector representing the wind direction at time t-1.
Step S320, obtaining a second correction parameter according to the geographical position information of the polluted air station at the t moment, the geographical position information of the relevant air station of the polluted air station at the t moment and the wind speed at the t-1 moment.
Specifically, the step of obtaining the second correction parameter according to the geographical location information of the polluted air station at the t-th moment, the geographical location information of the air station related to the polluted air station at the t-th moment, and the wind speed at the t-1 th moment includes the steps of:
obtaining the distance between the polluted air station at the t moment and the related air station thereof according to the geographical position information of the polluted air station at the t moment and the geographical position information of the related air station of the polluted air station at the t moment;
and obtaining a second correction parameter according to the distance and the wind speed at the t-1 moment.
Further, the second correction parameter is acquired based on the following formula:
wherein the content of the first and second substances,
representing a second correction parameter;
representing the wind speed at time t-1;
representing an air station acquisition cycle;
indicating the linear distance of the air station from the associated air station.
Step S330, according to the first correction parameter and the second correction parameter, correction processing is carried out on the diffusion matrix.
It should be noted that the product of the first correction parameter, the second correction parameter, and the diffusion matrix is the corrected diffusion matrix.
Further, a corrected diffusion matrix is obtained based on the following formula:
wherein the content of the first and second substances,
representing the corrected diffusion matrix;
representing a diffusion matrix.
And S140, obtaining a decision matrix of the polluted air station at the t moment according to the corrected diffusion matrix, and determining a pollution source air station of the polluted air station at the t moment according to the decision matrix.
It should be noted that the decision matrix represents the probability magnitude of the air pollution propagating from one air station to another.
Specifically, as shown in fig. 4, the step of obtaining a decision matrix of the air station contaminated at the t-th time according to the corrected diffusion matrix, and determining the contamination source air station of the air station contaminated at the t-th time according to the decision matrix includes the steps of:
step S410, transposing the corrected diffusion matrix to obtain a transposed matrix, and taking a difference between the corrected diffusion matrix and the transposed matrix as a decision matrix.
Step S420, if there is a decision matrix with a value greater than zero, determining the relevant air station corresponding to the maximum median value of the decision matrix as the pollution source air station of the polluted air station at the t-th time.
It should be noted that, when there is a decision matrix with a value greater than zero, which indicates that the pollution source of the polluted air station at the time t is propagated from elsewhere, the relevant air station corresponding to the maximum value in the decision matrix is determined as the pollution source air station of the polluted air station at the time t.
In step S430, if the values of the decision matrix are all less than or equal to zero, the contaminated air station at the t-th time is the contaminated air station at the 1 st time.
It should be noted that, when the values of the decision matrix are all less than or equal to zero, it is indicated that the pollution source of the polluted air station at the t-th time is in the area of the air station, and the polluted air station is the pollution source air station.
And S150, taking the pollution source air station of the polluted air station at the t moment as the polluted air station at the t-1 moment, and determining the pollution source air station of the polluted air station at the t-1 moment until the polluted air station at the 1 moment is obtained.
It should be noted that, after tracing back to the pollution source air station of the polluted air station at the time t, the recycling steps S120 to S140 trace back to the pollution source of the polluted air station at the time t-1 until tracing back to the polluted air station at the time 1, that is, tracing back to the pollution source.
The air pollution tracing method provided by the embodiments of the application comprises the following steps: acquiring historical air quality data of each air station in an area to be monitored, processing the historical air quality data based on a Gaussian process regression algorithm, and determining relevant air stations which meet the relevance condition and correspond to each air station; acquiring the t-th time quality data and the t-1-th time air quality data of the polluted air station at the t-th time and the relevant air stations of the polluted air station at the t-th time, and performing matrix processing on the t-th time quality data and the t-1-th time air quality data to obtain a diffusion matrix of the polluted air station at the t-th time; correcting the diffusion matrix according to the geographical position information of the polluted air station at the t moment, the geographical position information of the related air station of the polluted air station at the t moment, the wind speed at the t-1 moment and the wind direction at the t-1 moment; obtaining a decision matrix of the polluted air station at the t moment according to the corrected diffusion matrix, and determining a pollution source air station of the polluted air station at the t moment according to the decision matrix; and taking the pollution source air station of the polluted air station at the t moment as the polluted air station at the t-1 moment, determining the pollution source air station of the polluted air station at the t-1 moment until the polluted air station at the 1 moment is obtained, so that the pollution source can be quickly and accurately traced back, the prevention and treatment measures can be carried out on the pollution source more timely, and the further pollution deterioration is avoided.
It should be understood that although the various steps in the flow charts of fig. 1-4 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 1-4 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternating with other steps or at least some of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 5, there is provided an air pollution traceability device, comprising:
a data obtaining module 510, configured to obtain historical air quality data of each air station in an area to be monitored;
a relevant air station determining module 520, configured to process the historical air quality data based on a gaussian process regression algorithm, and determine relevant air stations that meet a relevance condition and correspond to each air station;
a diffusion matrix obtaining module 530, configured to obtain t-th time quality data of the contaminated air station at the t-th time and relevant air stations of the contaminated air station at the t-th time and t-1-th time air quality data, and perform matrix processing on each t-th time quality data and each t-1-th time air quality data to obtain a diffusion matrix of the contaminated air station at the t-th time;
the correcting module 540 is configured to correct the diffusion matrix according to the geographical location information of the polluted air station at the t-th time, the geographical location information of the relevant air station of the polluted air station at the t-th time, the wind speed at the t-1 th time, and the wind direction at the t-1 th time;
a pollution source air station obtaining module 550, configured to obtain a decision matrix of the polluted air station at the t-th time according to the corrected diffusion matrix, and determine the pollution source air station of the polluted air station at the t-th time according to the decision matrix;
and the circulation tracing module 560 is configured to use a pollution source air station of the polluted air station at the time t as the polluted air station at the time t-1, and determine the pollution source air station of the polluted air station at the time t-1 until the polluted air station at the time 1 is obtained.
For the specific definition of the air pollution tracing apparatus, reference may be made to the above definition of the air pollution tracing method, which is not described herein again. All or part of each module in the air pollution tracing device can be realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 6. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used to store air quality data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement an air pollution tracing method.
Those skilled in the art will appreciate that the architecture shown in fig. 6 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:
acquiring historical air quality data of each air station in an area to be monitored, processing the historical air quality data based on a Gaussian process regression algorithm, and determining relevant air stations which meet the relevance condition and correspond to each air station;
acquiring the t-th time quality data and the t-1-th time air quality data of the polluted air station at the t-th time and the relevant air stations of the polluted air station at the t-th time, and performing matrix processing on the t-th time quality data and the t-1-th time air quality data to obtain a diffusion matrix of the polluted air station at the t-th time;
correcting the diffusion matrix according to the geographical position information of the polluted air station at the t moment, the geographical position information of the related air station of the polluted air station at the t moment, the wind speed at the t-1 moment and the wind direction at the t-1 moment;
obtaining a decision matrix of the polluted air station at the t moment according to the corrected diffusion matrix, and determining a pollution source air station of the polluted air station at the t moment according to the decision matrix;
and taking the pollution source air station of the polluted air station at the t moment as the polluted air station at the t-1 moment, and determining the pollution source air station of the polluted air station at the t-1 moment until the polluted air station at the 1 moment is obtained.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
processing historical air quality data based on a Gaussian process regression algorithm to obtain a hyper-parameter of the historical air quality data;
performing regression processing on the hyper-parameters by adopting a maximum edge likelihood method based on historical air quality data to obtain correlation coefficients among the air stations;
and determining the air station with the correlation coefficient meeting the correlation condition with the air station as a relevant air station.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
obtaining a first correction parameter according to the geographical position information of the polluted air station at the t moment, the geographical position information of the relevant air station of the polluted air station at the t moment and the wind direction at the t-1 moment;
obtaining a second correction parameter according to the geographical position information of the polluted air station at the t moment, the geographical position information of the related air station of the polluted air station at the t moment and the wind speed at the t-1 moment;
and correcting the diffusion matrix according to the first correction parameter and the second correction parameter.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
obtaining a direction vector between the polluted air station at the t moment and the related air station thereof according to the geographical position information of the polluted air station at the t moment and the geographical position information of the related air station of the polluted air station at the t moment;
and carrying out vector processing on the direction vector and the wind direction vector of the wind direction at the t-1 th moment to obtain a first correction parameter.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
obtaining the distance between the polluted air station at the t moment and the related air station thereof according to the geographical position information of the polluted air station at the t moment and the geographical position information of the related air station of the polluted air station at the t moment;
and obtaining a second correction parameter according to the distance and the wind speed at the t-1 moment.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
transposing the corrected diffusion matrix to obtain a transposed matrix, and taking the difference value between the corrected diffusion matrix and the transposed matrix as a decision matrix;
if the decision matrix with the value larger than zero exists, determining the relevant air station corresponding to the maximum value in the decision matrix as the pollution source air station of the polluted air station at the t moment;
and if the values of the decision matrix are all less than or equal to zero, the polluted air station at the t moment is the polluted air station at the 1 moment.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring historical air quality data of each air station in an area to be monitored, processing the historical air quality data based on a Gaussian process regression algorithm, and determining relevant air stations which meet the relevance condition and correspond to each air station;
acquiring the t-th time quality data and the t-1-th time air quality data of the polluted air station at the t-th time and the relevant air stations of the polluted air station at the t-th time, and performing matrix processing on the t-th time quality data and the t-1-th time air quality data to obtain a diffusion matrix of the polluted air station at the t-th time;
correcting the diffusion matrix according to the geographical position information of the polluted air station at the t moment, the geographical position information of the related air station of the polluted air station at the t moment, the wind speed at the t-1 moment and the wind direction at the t-1 moment;
obtaining a decision matrix of the polluted air station at the t moment according to the corrected diffusion matrix, and determining a pollution source air station of the polluted air station at the t moment according to the decision matrix;
and taking the pollution source air station of the polluted air station at the t moment as the polluted air station at the t-1 moment, and determining the pollution source air station of the polluted air station at the t-1 moment until the polluted air station at the 1 moment is obtained.
In one embodiment, the computer program when executed by the processor further performs the steps of:
processing historical air quality data based on a Gaussian process regression algorithm to obtain a hyper-parameter of the historical air quality data;
performing regression processing on the hyper-parameters by adopting a maximum edge likelihood method based on historical air quality data to obtain correlation coefficients among the air stations;
and determining the air station with the correlation coefficient meeting the correlation condition with the air station as a relevant air station.
In one embodiment, the computer program when executed by the processor further performs the steps of:
obtaining a first correction parameter according to the geographical position information of the polluted air station at the t moment, the geographical position information of the relevant air station of the polluted air station at the t moment and the wind direction at the t-1 moment;
obtaining a second correction parameter according to the geographical position information of the polluted air station at the t moment, the geographical position information of the related air station of the polluted air station at the t moment and the wind speed at the t-1 moment;
and correcting the diffusion matrix according to the first correction parameter and the second correction parameter.
In one embodiment, the computer program when executed by the processor further performs the steps of:
obtaining a direction vector between the polluted air station at the t moment and the related air station thereof according to the geographical position information of the polluted air station at the t moment and the geographical position information of the related air station of the polluted air station at the t moment;
and carrying out vector processing on the direction vector and the wind direction vector of the wind direction at the t-1 th moment to obtain a first correction parameter.
In one embodiment, the computer program when executed by the processor further performs the steps of:
obtaining the distance between the polluted air station at the t moment and the related air station thereof according to the geographical position information of the polluted air station at the t moment and the geographical position information of the related air station of the polluted air station at the t moment;
and obtaining a second correction parameter according to the distance and the wind speed at the t-1 moment.
In one embodiment, the computer program when executed by the processor further performs the steps of:
transposing the corrected diffusion matrix to obtain a transposed matrix, and taking the difference value between the corrected diffusion matrix and the transposed matrix as a decision matrix;
if the decision matrix with the value larger than zero exists, determining the relevant air station corresponding to the maximum value in the decision matrix as the pollution source air station of the polluted air station at the t moment;
and if the values of the decision matrix are all less than or equal to zero, the polluted air station at the t moment is the polluted air station at the 1 moment.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), synchronous link (Sytchlite) DRAM (SLDRAM), Rambus (Rambus) direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the claims. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. An air pollution tracing method is characterized by comprising the following steps:
acquiring historical air quality data of each air station in an area to be monitored, processing the historical air quality data based on a Gaussian process regression algorithm, and determining related air stations which meet correlation conditions and correspond to the air stations;
acquiring t-th time quality data and t-1-th time air quality data of a polluted air station at the t-th time and relevant air stations of the polluted air station at the t-th time, and performing matrix processing on the t-th time quality data and the t-1-th time air quality data to obtain a diffusion matrix of the polluted air station at the t-th time;
correcting the diffusion matrix according to the geographical position information of the polluted air station at the t moment, the geographical position information of the relevant air station of the polluted air station at the t moment, the wind speed at the t-1 moment and the wind direction at the t-1 moment;
obtaining a decision matrix of the polluted air station at the t moment according to the corrected diffusion matrix, and determining a pollution source air station of the polluted air station at the t moment according to the decision matrix;
and taking the pollution source air station of the polluted air station at the t moment as the polluted air station at the t-1 moment, and determining the pollution source air station of the polluted air station at the t-1 moment until the polluted air station at the 1 moment is obtained.
2. The air pollution tracing method according to claim 1, wherein the step of obtaining historical air quality data of each air station in the area to be monitored, processing the historical air quality data based on a gaussian process regression algorithm, and determining the relevant air stations corresponding to each air station and satisfying the relevance condition comprises the steps of:
processing the historical air quality data based on a Gaussian process regression algorithm to obtain a hyper-parameter of the historical air quality data;
performing regression processing on the hyper-parameters by adopting a maximum edge likelihood method based on the historical air quality data to obtain a correlation coefficient between the air stations;
and determining the air station of which the correlation coefficient with the air station meets the correlation condition as the relevant air station.
3. The air pollution tracing method according to claim 1, wherein the step of performing matrix processing on each of the t-th time quality data and each of the t-1 th time air quality data to obtain the diffusion matrix of the air station polluted at the t-th time comprises the steps of:
and performing matrix processing on the quality data of each t-th moment and the air quality data of each t-1-th moment based on a random infection SIR equation to obtain a diffusion matrix of the polluted air station at the t-th moment.
4. The air pollution tracing method according to claim 1, wherein the step of performing correction processing on the diffusion matrix according to the geographical location information of the polluted air station at the time t, the geographical location information of the relevant air station of the polluted air station at the time t, the wind speed at the time t-1 and the wind direction at the time t-1 comprises the steps of:
obtaining a first correction parameter according to the geographical position information of the polluted air station at the t moment, the geographical position information of the relevant air station of the polluted air station at the t moment and the wind direction at the t-1 moment;
obtaining a second correction parameter according to the geographical position information of the polluted air station at the t moment, the geographical position information of the relevant air station of the polluted air station at the t moment and the wind speed at the t-1 moment;
and correcting the diffusion matrix according to the first correction parameter and the second correction parameter.
5. The air pollution tracing method according to claim 4, wherein the step of obtaining a first correction parameter according to the geographical location information of the polluted air station at the time t, the geographical location information of the air station related to the polluted air station at the time t and the wind direction at the time t-1 comprises the steps of:
obtaining a direction vector between the polluted air station at the t moment and the related air station thereof according to the geographical position information of the polluted air station at the t moment and the geographical position information of the related air station of the polluted air station at the t moment;
and carrying out vector processing on the azimuth vector and the wind direction vector of the wind direction at the t-1 th moment to obtain the first correction parameter.
6. The air pollution tracing method according to claim 5, wherein the step of obtaining a second correction parameter according to the geographical location information of the polluted air station at the time t, the geographical location information of the relevant air station of the polluted air station at the time t and the wind speed at the time t-1 comprises the steps of:
obtaining the distance between the polluted air station at the t moment and the relevant air station thereof according to the geographical position information of the polluted air station at the t moment and the geographical position information of the relevant air station of the polluted air station at the t moment;
and obtaining the second correction parameter according to the distance and the wind speed at the t-1 moment.
7. The air pollution tracing method according to any one of claims 1-6, wherein the step of obtaining a decision matrix of the air station polluted at the time t according to the corrected diffusion matrix, and determining the pollution source air station of the air station polluted at the time t according to the decision matrix comprises the steps of:
transposing the corrected diffusion matrix to obtain a transposed matrix, and taking a difference value between the corrected diffusion matrix and the transposed matrix as the decision matrix;
if the decision matrix with the value larger than zero exists, determining the relevant air station corresponding to the maximum value in the decision matrix as a pollution source air station of the polluted air station at the t moment;
and if the values of the decision matrix are all less than or equal to zero, the polluted air station at the t moment is the polluted air station at the 1 moment.
8. An air pollution source tracing device, comprising:
the data acquisition module is used for acquiring historical air quality data of each air station in an area to be monitored;
the relevant air station determining module is used for processing the historical air quality data based on a Gaussian process regression algorithm and determining relevant air stations which meet the relevance condition and correspond to the air stations;
the diffusion matrix acquisition module is used for acquiring t-th time quality data and t-1-th time air quality data of the polluted air station at the t-th time and relevant air stations of the polluted air station at the t-th time, and performing matrix processing on each t-th time quality data and each t-1-th time air quality data to obtain a diffusion matrix of the polluted air station at the t-th time;
the correction module is used for correcting the diffusion matrix according to the geographical position information of the polluted air station at the t moment, the geographical position information of the relevant air station of the polluted air station at the t moment, the wind speed at the t-1 moment and the wind direction at the t-1 moment;
a pollution source air station obtaining module, configured to obtain a decision matrix of the polluted air station at the t-th time according to the corrected diffusion matrix, and determine the pollution source air station of the polluted air station at the t-th time according to the decision matrix;
and the circulating source tracing module is used for taking the polluted source air station of the polluted air station at the t moment as the polluted air station at the t-1 moment, determining the polluted source air station of the polluted air station at the t-1 moment until the polluted air station at the 1 moment is obtained.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor realizes the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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